Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

How machine learning with open source tools helps everyone build better products

Michelle Casbon (Qordoba)
2:55pm3:35pm Thursday, September 28, 2017
Data science & advanced analytics, Machine Learning & Data Science
Location: 1A 06/07 Level: Intermediate
Secondary topics:  Text
Average rating: ****.
(4.00, 4 ratings)

Who is this presentation for?

  • Engineers, data scientists, designers, and product managers

Prerequisite knowledge

  • Familiarity with system architectures, distributed computing tools, machine learning, and NLP (useful but not required)

What you'll learn

  • Explore Qordoba’s architecture for handling billions of localized strings in many different languages
  • Understand how to make your own products better with localization


Building products that feel native to every user, regardless of language, is the best way to establish a user base across the globe. To do this, a product needs to support a variety of locales. The challenge with supporting multiple locales is the maintenance and generation of localized strings, which are deeply integrated into many facets of a product.

Michelle Casbon explores the machine learning and natural language processing that enables Qordoba to generate high-quality translations in many different languages and describes the techniques Qordoba uses to provide continuous deployment of localized strings, live syncing across platforms (mobile, web, Photoshop, Sketch, Help Desk, etc.), content generation for any locale, and emotional response. Michelle also explores Qordoba’s architecture for handling billions of localized strings in many different languages, using Apache Spark and Apache PredictionIO (incubating) for natural language processing, Kubernetes and Docker for containerized deployment, scaling, and management, Apache Cassandra and MariaDB as a storage layer, and Scala and Akka as an orchestration layer.

Photo of Michelle Casbon

Michelle Casbon


Michelle Casbon is director of data science at Qordoba. Michelle’s development experience spans more than a decade across various industries, including media, investment banking, healthcare, retail, and geospatial services. Previously, she was a senior data science engineer at Idibon, where she built tools for generating predictions on textual datasets. She loves working with open source projects and has contributed to Apache Spark and Apache Flume. Her writing has been featured in the AI section of O’Reilly Radar. Michelle holds a master’s degree from the University of Cambridge, focusing on NLP, speech recognition, speech synthesis, and machine translation.

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Picture of Michelle Casbon
10/04/2017 4:18pm EDT

Thanks, Bostjan – glad you were there! Slides have been uploaded & should appear here shortly.

10/04/2017 1:34am EDT

Dear Michelle, it was an excellent sesion. Can you please share the slides?